Powerhouse Effect: Design Thinking + Data Science
Infusing data science into design thinking can de-risk your innovation efforts.
Advancement in technology is shaping our future and data is at the center of everything especially as we are busy creating new data at an astonishing speed. In fact, in the last two years 90% of the world's data has been created and nearly 2.5 quintillion bytes of data are produced by humans every day. Data has always been an integral part of innovation and design but its time to further evolve how we approach design and innovation using data science. In this write-up I hope to shed some light on ways we can leverage data science to take design thinking to a totally new level.
Design Thinking is a mindset and some call it a non-linear framework that explores problems worth solving and solutions worth launching. Where as Data Science is a data discipline focused on analyzing large volumes to identify patterns at a large scale and craft insights that can enhance a process, product, or experience.
In design thinking, problem framing and context development kicks-off the creative process and we all know how important this is to position a project/engagement and define success. Traditional methods heavily relied on static data, institutional knowledge, and basic feedback mechanisms to frame the initial problem statement. Data science could take this to a totally different level and further fine tune the problem at hand. Taking vast amounts of data available internally and from third-parties one can create new insights and patterns that can change our understanding of the problem, data driven problem statements can easily accelerate the speed of innovation, reduce the cost, and de-risk your engagement.
Once we figure the areas to focus we move into deepening the understanding through Empathy for users/consumers and this in the past relied heavily on qualitative with some quantitative ways to map the learning from field interviews, workshops, and other techniques. Data Science and AI especially can not only segment and identify the population you can target for your research and analysis. AI has the ability to extract real insights from written comments to generate new insights. When we combine AI/ML insights with human insights and perspectives we may land on some unique opportunities. Some other areas of examples are continuous survey mechanisms, virtual sentiment analysis and large-scale data memory can help co-relate needs faster than humans can.
Post landing on key insights and points of view, we move into divergent thinking to generate ideas that solve our new hypothesis driven problem statements. There are nearly a 1000 different ways you can go about ideations, the general notion is to take one ide/hunch and marry it to others to make a broader solution which could be a product or service or experience. I see an opportunity to leverage data science to catalogue ideas, co-relate and combine, and map to problem statements. I also see an opportunity where we can feed latest innovation and tech data into a DB and find solutions that may be ready to buy instead of build.
Rapid prototyping is next, once an idea or a group of ideas hit your gut as a potential solution to an unmet need – get it out into the world as quickly as you can to validate and pivot. I can’t tell you how many tools are in this space already, there are AI driven tools from build (Figma, 3D printers, pure hand prototyping etc.) through test/feedback loops (Suzy, Justinmind, Invision and many more). We need to catalogue the test feedback and results in a way that could be leveraged in the future which is where data science could play a role. Depending on the idea there could be many more applications of data science +AI + ML. The solution itself could be a model or powered by one. Another interesting space is to capture and catalogue experiments and feed you AI this data to add value to the earlier parts of this process.
Minimum Marketable Product is what is built and launched to make a real impact. Using data and design driven approach can further streamline what we call and MVP and a better definition of success. Based on the solution being explored this could be anything from physical to digital. Data science could have a role in this as well especially around consumer analytics, consumer behavior, interactions, end-less opportunities, feedback automation and analytics to take data from your product and evolve.
While design brings its free-flowing creative spirit to the show, Data acts as a solid guide/fact-based decision maker throughout the process and together they create innovation.